duke_ultrasound

Deskripsi :

DukeUltrasound adalah kumpulan data ultrasound yang dikumpulkan di Duke University dengan probe Verasonics c52v. Ini berisi data beamformed delay-and-sum (DAS) serta data pasca-pemrosesan dengan Siemens Dynamic TCE untuk pengurangan bintik, peningkatan kontras, dan peningkatan struktur anatomi yang mencolok. Data ini dikumpulkan dengan dukungan dari National Institute of Biomedical Imaging and Bioengineering di bawah Hibah R01-EB026574 dan National Institutes of Health di bawah Hibah 5T32GM007171-44. Contoh penggunaan tersedia di sini .

Beranda : https://github.com/ouwen/mimicknet
Kode sumber : tfds.datasets.duke_ultrasound.Builder
Versi :
- 1.0.0 : Rilis awal.
- 1.0.1 (default): Memperbaiki penguraian harmonic bidang boolean.
Ukuran unduhan : 12.78 GiB
Ukuran dataset : 13.79 GiB
Di-cache otomatis ( dokumentasi ): Tidak
Perpecahan :

Membelah	Contoh
`'A'`	1.362
`'B'`	1.194
`'MARK'`	420
`'test'`	438
`'train'`	2.556
`'validation'`	278

Struktur fitur :

FeaturesDict({
    'das': FeaturesDict({
        'dB': Tensor(shape=(None,), dtype=float32),
        'imag': Tensor(shape=(None,), dtype=float32),
        'real': Tensor(shape=(None,), dtype=float32),
    }),
    'dtce': Tensor(shape=(None,), dtype=float32),
    'f0_hz': float32,
    'final_angle': float32,
    'final_radius': float32,
    'focus_cm': float32,
    'harmonic': bool,
    'height': uint32,
    'initial_angle': float32,
    'initial_radius': float32,
    'probe': string,
    'scanner': string,
    'target': string,
    'timestamp_id': uint32,
    'voltage': float32,
    'width': uint32,
})

Dokumentasi fitur :

Fitur	Kelas	Membentuk	Dtype
	fiturDict
das	fiturDict
das/dB	Tensor	(Tidak ada,)	float32
das/gambar	Tensor	(Tidak ada,)	float32
das/nyata	Tensor	(Tidak ada,)	float32
dtce	Tensor	(Tidak ada,)	float32
f0_hz	Tensor		float32
final_angle	Tensor		float32
radius_akhir	Tensor		float32
fokus_cm	Tensor		float32
harmonis	Tensor		bool
tinggi	Tensor		uint32
initial_angle	Tensor		float32
radius_awal	Tensor		float32
menguji	Tensor		rangkaian
pemindai	Tensor		rangkaian
target	Tensor		rangkaian
timestamp_id	Tensor		uint32
tegangan	Tensor		float32
lebar	Tensor		uint32

Kunci yang diawasi (Lihat as_supervised doc ): ('das/dB', 'dtce')
Gambar ( tfds.show_examples ): Tidak didukung.
Contoh ( tfds.as_dataframe ):

Kutipan :

@article{DBLP:journals/corr/abs-1908-05782,
  author    = {Ouwen Huang and
               Will Long and
               Nick Bottenus and
               Gregg E. Trahey and
               Sina Farsiu and
               Mark L. Palmeri},
  title     = {MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box
               Constraints},
  journal   = {CoRR},
  volume    = {abs/1908.05782},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.05782},
  archivePrefix = {arXiv},
  eprint    = {1908.05782},
  timestamp = {Mon, 19 Aug 2019 13:21:03 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1908-05782},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Deskripsi :

Beranda : https://github.com/ouwen/mimicknet
Kode sumber : tfds.datasets.duke_ultrasound.Builder
Versi :
- 1.0.0 : Rilis awal.
- 1.0.1 (default): Memperbaiki penguraian harmonic bidang boolean.
Ukuran unduhan : 12.78 GiB
Ukuran dataset : 13.79 GiB
Di-cache otomatis ( dokumentasi ): Tidak
Perpecahan :

Membelah	Contoh
`'A'`	1.362
`'B'`	1.194
`'MARK'`	420
`'test'`	438
`'train'`	2.556
`'validation'`	278

Struktur fitur :

FeaturesDict({
    'das': FeaturesDict({
        'dB': Tensor(shape=(None,), dtype=float32),
        'imag': Tensor(shape=(None,), dtype=float32),
        'real': Tensor(shape=(None,), dtype=float32),
    }),
    'dtce': Tensor(shape=(None,), dtype=float32),
    'f0_hz': float32,
    'final_angle': float32,
    'final_radius': float32,
    'focus_cm': float32,
    'harmonic': bool,
    'height': uint32,
    'initial_angle': float32,
    'initial_radius': float32,
    'probe': string,
    'scanner': string,
    'target': string,
    'timestamp_id': uint32,
    'voltage': float32,
    'width': uint32,
})

Dokumentasi fitur :

Fitur	Kelas	Membentuk	Dtype
	fiturDict
das	fiturDict
das/dB	Tensor	(Tidak ada,)	float32
das/gambar	Tensor	(Tidak ada,)	float32
das/nyata	Tensor	(Tidak ada,)	float32
dtce	Tensor	(Tidak ada,)	float32
f0_hz	Tensor		float32
final_angle	Tensor		float32
radius_akhir	Tensor		float32
fokus_cm	Tensor		float32
harmonis	Tensor		bool
tinggi	Tensor		uint32
initial_angle	Tensor		float32
radius_awal	Tensor		float32
menguji	Tensor		rangkaian
pemindai	Tensor		rangkaian
target	Tensor		rangkaian
timestamp_id	Tensor		uint32
tegangan	Tensor		float32
lebar	Tensor		uint32

Kunci yang diawasi (Lihat as_supervised doc ): ('das/dB', 'dtce')
Gambar ( tfds.show_examples ): Tidak didukung.
Contoh ( tfds.as_dataframe ):

Kutipan :

@article{DBLP:journals/corr/abs-1908-05782,
  author    = {Ouwen Huang and
               Will Long and
               Nick Bottenus and
               Gregg E. Trahey and
               Sina Farsiu and
               Mark L. Palmeri},
  title     = {MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box
               Constraints},
  journal   = {CoRR},
  volume    = {abs/1908.05782},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.05782},
  archivePrefix = {arXiv},
  eprint    = {1908.05782},
  timestamp = {Mon, 19 Aug 2019 13:21:03 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1908-05782},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}